1. Technical Field
This disclosure relates in general to a compression method, and more particularly to a method and a device for compressing a feature descriptor.
2. Description of the Related Art
A feature descriptor is a descriptor, which can best represent a feature point, and may be obtained through various ways, such as scale invariant feature transform (SIFT) for performing Gaussian blur and subsample many times on an input image whose feature points are to be captured. Thereafter, an image difference between the input image and the blur image with the same resolution is performed to generate many octaves of difference of Gaussian (DoG). Next, the pixel with the maximum or minimum pixel value in the DoG image greater or smaller than the neighboring 26 pixel values is found according to the DoG image of the neighboring layers, and such the pixel point is a location where a feature point locates.
The SIFT creates a window according to the position of the image where the feature point is located after finding the location of the feature point, and calculates the intensity gradient vector between the neighboring two pixels in the block. Thereafter, statistics for the histogram of the gradient vector in the window is complied to find the peak gradient direction in the histogram, and this direction serves as the orientation of the feature point, and the subsequently generated vector direction of the feature descriptor is represented by an angle with respect to this orientation. Next, the window is cut into 4×4 subblocks, and the histogram of the gradient vector in each block is counted, wherein each histogram has eight gradient vector directions (8 bins), the value of each gradient vector direction is converted into a vector value of the feature descriptor after weighting and normalization. Thus, there are, in total, 4×4×8=128 feature descriptor vectors, which are also referred to as 128 sets of data, in the feature descriptor. Because each feature descriptor is composed of 128 sets of data, if the width of each set of data is 1 byte, each feature descriptor needs 128 bytes.
European Patent Number WO2009133856A1 discloses a method of creating an image database for object recognition, in which six smaller bits after the feature descriptor is quantized is omitted, and the representation is changed to two bits. U.S. Patent Publication No. US2010/080469A1 discloses a method of reducing the feature descriptor data, in which the feature descriptors of the gradient vectors of five sample points are adopted, and the number of the vectors of the feature descriptor is reduced from 128 to 40. Chinese Patent Number CN101661618A discloses a method for extracting and describing image features with turnover invariance, which mainly adopts a feature descriptor with extension covering 9 units to decrease the number of vectors of the feature descriptor from 128 to 83.